Implement Image Recognition with a Convolutional Neural Network
Image recognition is used in a wide variety of ways in our daily lives. This course will teach you how to tune and implement convolutional neural networks in order to implement image recognition and classification on a business case.
What you'll learn
Image recognition has an extensive and important impact on our daily lives. From unlocking phones using facial recognition to detecting anomalies in chest-x rays, it is everywhere.
In this course, Implement Image Recognition with a Convolutional Neural Network, you’ll understand how to implement image recognition and classification on your very own dataset.
First, you’ll be introduced to the problem and dataset. Then, you’ll learn how to explore and prepare the dataset for the next step. Next, you’ll see how to build, train, and test a neural network on the dataset. Finally, you’ll explore how image augmentation and transfer learning help to lift the performance metrics involved in your solution.
When you’re finished with this course, you’ll have the knowledge required to implement image recognition on any dataset of your choice.
Table of contents
- Module Introduction 1m
- Better Performance – When and How? 2m
- Procuring Additional Training Data – Image Augmentation 5m
- Hyperparameter Tuning 3m
- Overfitting and Underfitting 3m
- Demo: Image Augmentation and Hyperparameter Tuning 8m
- What Is Transfer Learning? 4m
- Transfer Learning – When and How? 4m
- Demo: Improving Performance through Transfer Learning 3m
- Summary 1m